{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01a18183-9056-4e47-a8d7-806ebd4789fe",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "711a5065-38a7-4f4e-b73d-c7cfda845ad8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"5G移动用户使用预测挑战赛公开数据/train.csv\")\n",
    "test_data = pd.read_csv(\"5G移动用户使用预测挑战赛公开数据/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8e1d311e-24fd-4db5-97cc-c0aedc6820d3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AIkhXbJenvLKEWBmGYcRGfGGnVaMsDwRmXoggfPQRInoewH9jq3TFMwDWZST/K4CPhXMODs9S5NcAfD4s6ybEFGJlGIYRG6lU9E9CqRXpirKEWBmGYcRGgt/8o5KEeQgVQ9oWtVWv3hZG53ZF9+FlN4JOXdksijqnRPoMLp33fS/N4vf/m7P9QGqsl0bKWURpSpSQ0kGlIJlNERxFW8pt0IJmt6D3bfE7sEFYzgeVwWxHvVtba1pbYcvN16ykkeqda5RfhezTYmzZ2vUmj0q7pUT5kUp5Bu10tkY4x9I/oCkCd4u6WpRy14l4WukT6ldaKP1j2rXUHuGeu1L8hscov+Fifp95SbCzOCqJeiCEMhUnid1/CmcwG4ZhJJdhMEKoWDwKEc0iovflSfYYgpekvQF8n5n3H3oYENE9RLSBiP5W7rYahmEUDHP0T0KppLjdLAD5HghvIVjr4A/Kd1cA+HS8TTIMw4iJYRBlVLLJKE6JCmZeGpbp9RgzPxDOXygaWagmHV0vjMOrlUD2deRGmE9W1huTT1rNZin3SRuq9BcAwK6PX+VsL5nxbS/Ny83uaY3iH9DstRItzj8KjaJPB0SA+cJm/zIcL4L4J0SQBWhQ3rykNIT0FwDAZnGyNipLwzVxfvu2POdSslubS+HJqSh9LI9cK0fm02z0zeIYtLdB6R/YrEwGkPlI8VINinwbpcyIUnefSNOiOmrcfdqtVXqk3qj3r50p6TJYy7d16YoySFQYhmHUJKwELNQapZqM4paoKAmTrjAMo2qYyUjlZxASFWWoQyVzWTpNukKGAZ5wrv88vOZX7slqU4atUwZdE5GmnCgXMNfMU1L9ckcR16mFlEoT0VELf+CluWjfM5ztMfWtXho5zG8ifxAvD2sT+8IGdaKcY2mcl0aqc44TYYAbNbOEWCi9mZX2iWzLG/00MoxSmocA4IV6V/dTmu4AAPXuT2W8FkMq6KhzT/rYtF95FNPicnL7fXrKN1HKHuxX+rRHLHk3SrkmV9S5Zo/1ipjFnkL5VZP6gDhWWe549m89XcJSrL2tRrmVThXn5vtv+wLKF0+aFaGkAhkGYaeljhDilqgwDMOoTdIc/ZNQSnogxC1RQUTvIaLlCOYi/CoUtRv67jEAfwJwFBEtJ6J/KaXthmEYsTI4GP2TUEo2GcUsUfE0Ah+E9l02nSPDMIzqk+D5BVFJ1EzlciOHQ+fM6fHSzEq79va2lH+Su4RNvFkxHbZFkGaWK5utE+VqfgcZUir9BQDwxILrnO3BR2/2CxpwbcO8YrmfRgxtaa+9/TQi1O7P5zznJZE+ljYRF7ij8juSIZtvNfodKG3XcoUyrW4tzUne0mHKORc2+Y4GJdRSbB+yReRRYi3ldTFWad97026Hvdbg+xAm97kZ5SprANArnC4bFNn22Zvdusa2+uuqvdLnriu4WumLHURnzBDnvEcJ7W0VztZNdX6HdUS4Y0m561d296/bJzeUQbsiRmcxER0D4CcIInSvYebLlTQnI7DIMIDnmflTpdZblQeCSVQYhjHsiMk3QET1AK4C8CEAywE8TURzQ5HPoTTTAXwLwGHM3ElEE+KouyrSFcx8WShLkfm5jIiOIKJniWiQiE5UymgP/Qc/r1S7DcMwIhHfimkHA1jEzIuZuR/AzQBmizSfB3AVM3cCQK7FwgqhlqQrAOB7AB6Nr0mGYRgxUUCUUeacqfBzdkZJkwEsy9heHu7LZA8AexDRP4joydDEVDI1I11BRAcB2AHAPQBmltpuAGgj//B9KQHf1uhJFivmSFmOliaVR0NZ+1bKUGhzDKTPoOGIT/pp/nGru6PVLwfd3c4mL1viJeGOTme7XrG/13nSFe73UWQzNLkGbRnGYkiJnh5QpRjySyZI+sSy31Gaq10n/WIOhla3dp1KpHy5Vs4WcTZ6+3x/Rb+oS6tZnmPZF31ae4XPQNYD+Mtqaj8h2Ye9vU1508QBD0Zf+CZzzlSRNACYjuBFewqAR4loH2beUEKZtSFdQUR1AH4M4DQAR+dIdzaAswHgU6MPxuFt0ws/KMMwjGKIb2LaCgBTM7anYOvcriGWA/gnMw8AWEJEryF4QDxdSsW1Il1xLoC7mFkJhdkKM89h5pnMPNMeBoZhVJT4JqY9DWA6Ee1CRE0APglgrkhzB4LRAYhoHAIT0uJSD6FWpCveC+BwIjoXQBuAJiLqYeYLCikkrue3LKfYp2ocw1ZNaVKGlHrmIQANh7k++4Fbf+KXPWWqs51e7JuM6nac6KbBSi+NPE4ZcZhWjiFK30j1iGLPgzQZsdKeSkWY6+qd8dg3ZD9HkYZQzZpiV5S+kWnK2Z/y+opgTYuHmMJOmXmQiM4DcC8Ci+q1zLyQiC4FMI+Z54bffZiIXkIg8PoNZu4ote5SHwgPAridiP6bmTsiSFe0F1MJM5869DcRnQFgZqEPA8MwjLISoyQFM98F4C6x7+KMvxnA+eEnNmpGusIwDCPRxBd2WjVqRroiI811AK4rqJGGYRhlppAoo6SyTUlXSKkDTeZY2qU1W7YM/exX0shgPe2dIN97grbSmQzR1GSrPRkKJaRU+gwaT/x3L03q5X8427RCBjoAvG6ds92kSCb0Czu0lJNoUeRBZKjqCC+Fj7K4nScnoZmTvZXOYjI613l9kT+EOYrRQRvWy7BYPV/h7dGI0kYpM94qfkgyBBaAJ7Styb3kidQG4EuIb1JCZ8sRdppkFdOomHSFYRhGHAyDB0JNSFcQ0ZGh72Hos4WIjq9U2w3DMPJiPoSCmAWgB8ATOdIMSVd8PXMnMz8EYH/gnUV4FgG4r9AGRDkNykJTeamk/kekuuWbiphxDPghpdI8BAD1ex3mpnnoHr+cCXKFtD6tRWVBns8oM57Vxb2KqEstu0zhjZW8fci6WDkoZeG3RFGpKFOPYTBCqBnpigxOBHA3M/eW2nbDMIy44MHkvvlHpSakKwSfRBDeqrXHpCsMw6gOMa6HUC1KHSF40hVEtA+APxLRJASjBH96a5GEZe6DYJaeR6Zg1NVTT6v98ZthGLWDmYxUyiFdMcTJAG4PBZ0KRp6uPsU6K82jWijjQAQbqpw+XxchfFWihubJkDqlK+TKZppKqZSh0EJKpc+g6dzv+W28352C0k/+7Hl5HC2i26NIV8hVsADfZ6CdFs8ErvS5zNeiOP3SSnivRPqfGkVlmm07ih+kBTK+3f/ZynDfRiX8ty+CdV22eUSjf301Dza7O5SLW65M1yLbo7xMyzRppdwtfjYP2Tstjf4axnXlWNZ4GDwQSnUPPQjgJCIaC7zj8M0lXbFdifWdAuCmEsswDMOIHWaO/EkqNSNdQUTTEEjCPlJKmw3DMMpCfGqnVaNmpCvCCCS5apBhGEYi2OajjGoNacuuiyliWZURLqLoCOZupW5tKTbXQCpXNQN82WopQQH4cwykvwAAGj50urOdoou9NPl+JsW+L3mr0kXJo3RXFHNyMW2UvpFij3MgwkA+Sl/IY9fSSOnvlDLpIMq1nS+JWkZML86e/07uKBcJfvOPiklXGIZhxEHtDxCq80AIb/wF3fxDh/UfAUwDsBTAyczsv/oahmFUAR4GIwRKssc7EyL6EYD1zHw5EV0AYAwzfzNbem0eQqt4gmtqin+r2+hsz8IoL81JU9wQzbvf8l0bsmxtQfiuutxp2pU3jk4Rl6i9lIwXNhBt4XtpztBUSiXaoudSHfaU5y/10rx5xBed7ct625ztM7f45XbAXRj9vXv5YbF14nXmsRf889AqQkg148t+e612tm9e7LuxZKisupCY6MJiVnTT4qlb8pQL+GavHqWy0eIYNJmWJrFvk1LORmGCaVE64426fmd7KrvnczX5hrpW0UNtSrljItirXqx3635XuslLM0pE8p6x4vcl2483nHJk5Jvp6JseqprCRi5KijIiomlE9DIR/ZqIFhLRfUQ0gogeJqKZYZpxRLQ0/PsMIrqDiO4noqVEdB4RnU9E84noyXAUkI3Z2Oq8vh7A8aW03TAMI1bSBXwSShwyVdMBXMXMMwBsAHBCnvR7A/g4gPcgMBv1MvMBAP4PwGdy5NuBmVeFf68GsINMQERnE9E8Ipr3WM/rhR2FYRhGCfAgR/7kg4iOIaJXiWhRaBHJlu4EIuKhF/BSieOBsISZnwv/fgaBjT8XDzFzNzOvBbARwfwEAHghQl4A76wn6vUqM89h5pnMPNN0jAzDqCSc5sifXBBRPYCrABwL4N0ATiGidyvptgPw7wD+GdcxxOFUztQ7TiFQNh3E1odNS4706YztdJ72rCGiScy8KtQ0ervQhvZGePztWedOph7X76d5bMmOznaLYg3sEUfSrAwTxwg7ZofwD7Sl/EyNwq66sd6vXK5IJlcfA/ywP7mqmYYmpSFbKP0FALDzo790tg85wA1NnXHIMi/P4mfGONuLXh7vpdn/LLdBR+/qy2bwFtdW3bjPLl6aN3/lrig3ecD/wUqfUI8iq6DZ9jPRLAVSGmVcBMdkl1L3zmL5xgEl8HNtQ/4fQJtXv3ZQ+a+V/QZdu71crZAa/J+6rFn6M4Bob7C7CZ+B9P8A+WVjiiI+U9DBABYx82IAIKKbEZjMXxLpvgfghwC+EVfF5VI2XwrgoPDvE3OkK4S52CqFcTqUyW+GYRjVopD1cTLN2+Hn7IyiJgPIfEtaDjEpl4gOBDCVme+M8xjKFXZ6JYBbwoOMq8GXh2WeBeBNBEJ3hmEYyaCAEUKmMnOhEFEdApmgM4rJn4uSHgihnMTeGduZax1kylR8J/z+OgDXZaSflvG3851SVweAo0ppr2EYRrng+BRUVyDQbRtiCrYKhgKBSOjeAB6mIBR8IoC5RHQcM88rpeJtSrqiUxjIZHw3ABwq9HWP+vQmL83nb3EzHjcw0kvTJcysx43zXR4TPjra2b75eldWeEGzb6sdEMHj45S47Dbhm9DmW8jZ/NLvoKHZYiVyjgHg+wzOmu/OVbh134u8PClhp76q3p+HcMFvJjnbW0gu5wl0CR9L3QO+gHJ7yvVXfHDmci/NY0+7cxwGlDkZUqJ71373DrGu3v+5yTkik1K+06pRnPMlnlsOOOqSCe4ORcL7zkvXO9vrFf/TRHaXQB3d5C+J2tDgXmCvdo/20nz4IjeCPPXCImd7YNVmL0/Xm67tv3N9q5fmKXZ9fJqf4eN7uj6pR1/y55VoPqBSiXGp5KcBTCeiXRA8CD4J4FPv1MO8EcA7FzsRPQzg66U+DIAEPhCI6CoAh4ndPwGwKwLHShqBQ/kMZl5Z4eYZhmGoxPVAYOZBIjoPwUJg9QCuZeaFRHQpgHnMPDeemnwS90Bg5n/T9hNROzNfFP79ZQAXI1in2TAMo+rEOEIAM98F4C6xz1eODPbPiqvekqQrwjUK7gbwOID3IRjezA73fZ2Z5xHROARPtWlEdAaCGcYjEUxouxLBMpufRhB++hFmXo88ENG3AOzEzH6MY0iUJTR7lFFjrxieT1ViLeUeTZZCSkxoaZrFvqUiFu59W+RKWcDCZvcZvlHRH9hLWB202f5SuqIugtSktrKZzLVz2jfJzDhkrbN979PuEP7EBf5KbOtPPNPZfvA1X5Ziep1vzpMMCrVObfWswZSb5q1B31TRJ0wMvYrJQZrmpFRKn2a6E9vtqfznYYNi6pHhoprA52bRZq09UweEUq5yzjfUuxd3t7LMm7zeR4nj0sKlo9xPZWivtqKh7AvttydXlJu9+g8l25DWzJoV+Wa6w8MPDz/pipBKzVQGEV1GRMsAnIpghGAYhpEICgk7TSo1NVOZmS9k5qkAbgRwnvzepCsMw6gW6UGK/EkqcTwQ5EzlBpRnpnImN0IZiZh0hWEY1YKZIn+SSrmcyksRzFR+CjHNVCai6cw89No/G8ArhZYhrcda2Km0q2r2x83iMZpPsgDQO1o+jVuEvbZBsapKaeu6BiX8McJjXvoVNHmLfHk0OhSpYSlDIUNKpb8AALa/9Vpne2A/30K4fsB919DCwAfI7YyGLfnNvH2aHIgIM40y6pc+hSh5Nim+CSmzEKWcQSUsVrZHO+UbhX9A8xv1Rzgu6cOQdWvnqpi3U63uLeLYNSnw5jLclJNsCopKTc1UJqJ3IbgG3oRFGBmGkSC4mHVzE0YtzVTO56w2DMOoGjWy1lhOEjcPoZxIdVEt1PKVencw29nkjzdP7Hdnbz5br4QpipcFzYyzjtwGTUq7w/VBZRA9IeW2r5n9mL+3Gkmk8euWjMifxJuJq/FRZWUzqVQqZx3XKyGl0kSkrcT27L5fd7ZfJf88yPBCLaxzSZPbh0vr/XDfcaKf5bWk8aaw9WgrgEnTSmedb3foEYaRaWn/Z/t8g7vW2mr2w38PgjvLd6zyRvuimPq7hHu9NI3CDDeD/avnNrihxrvVtzvbCwbWeXl2anBXJ5xKfrm7psT1rlyTD9a7bd5FKWdiFDtvgWzzI4RykG2mMjP/thrtMQzDiEK6DA+ZSpO4B0KOmcpfAvBvCCKZ7mTm/6howwzDMHJgI4QKQURHIogs2o+Z+4hoQr48hmEYlSTJ4aRRKfqBUGHZii8CuJw5kGJk5oJXSwP8KfatyhO9VdjtZ28Z8NJ0c6O7Q5m6L5mhyFA0Ch/GIhGx2VGfv2BNokCuTqWFzkYZ3UprttYamaZOuaLkymZSpXRygy9BIUNKpb8AAA5ccKWzPU0JX40yCWjvXvfI5q/03zf6hESIVFEFALnY2ATRyfK8AP65mapI07awe02uU/r4KyO7nO1Nm5q9NPPS+UNn9xfOr/3h+2WkBIbmHzt3YKy7Q/yM9mv0/UYd4vcwIsIbtyZdcSC7be5U5F3ayhAiOhzCTkudmFYp2Yo9ABxORP8kokeI6D0lttswDCNW0kyRP0ml1AdCpWQrGgBsD+BQBOuH3kLkz7wx6QrDMKpFOlUX+ZNUSm1ZpWQrlgO4jQOeCtN7q6GYdIVhGNWCOfonqZTDqbwUMctWALgDwJEAHiKiPRD4HvxA5jxIU6I2pV3OBVhOfqK1Is5fs0cOCIN7R4NvgZf2T18+2b9yGsTVtLzRL3dEBFumPCrNFitL1t4eZFWPveDbho/etcPZ1lY2k0hpA22OgfQZSLkLAOg+67POdn+nP1xfvnK0s72pLv97ktbF0hIg53+oKyxKqRTFmjAgEmnlLFo/WitdFJ4/iaRfkcCQ3jCtPVIuXKJdb3JehCqJIbY164uUHdfkN7RVBEtlOEQZlWPsciWALxLRfChv8UVyLYBdiehFADcDOJ1LWcjBMAwjZoaDD6HoEUKFZSv6AZxWbFsNwzDKzTYddjoc0CQdpDyDNvSV4YNSjVIrW1udSna+HEZvrPcHcDLMVFtkvK+IcZ9qzhBEud5bldg73uKWLkM25apmgK9SKiUoAD+kVJqHAGC737gT3B+e8S0vTb8YKGtDzyhKoRJ5zrXrTfb7QJEqpZvqpEqpjww1VqIxPbXVZqXfpRlJu9yk4qg0P2ommyiroUWhWvfl4WCzSJS7m4iuIqLnxMf/lRuGYSSMVLou8icfRHQMEb1KRIuI6ALl+/OJ6CUiWkBEDxDRznEcQ6JGCDlkK/YDcDWANgRO61OZuUtLaxiGUQ3iGiEQUT2AqwB8CEGE5dNENJeZX8pINh/ATGbuJaIvAvgRgE+UWneiRgg5uAbABcy8D4DbEcxFMAzDSAwxOpUPBrCImReH/tObEahAvAMzP8T8jhTtkwCmxHEMtSJdsQeAR8O/7wdwL4CLCm2zPA99SpookWPSPrtW6UXpV4giQS1pVsJOpY1Zrt4GRAs7lWiH7dWuHIO38ppSTuM+u7hpHnClmVsafQ+GXNlMk62WaCGl0mcwa+F/eWn+MeObzvYGRaRDSkwMan0htqV4hCYXIu3kmqq29BOpIcLy9VTxRchj0Oz4baKfexQfmhf66RcDqRgi/RXadeL9HjW/W4TfkbyaKvXWW4hTOVw87OyMXXOYeU7492QAyzK+Ww7gkBzFnYXgvlsytSJdsRBbn5AnAZhaQpsNwzBip5ARQuYk2vAzJ38NPkR0GoCZAK6I4xhqRbriTADnEtEzALYD0K8lMukKwzCqBRfwycMKuC+9U8J9DkR0NIALARw3JPxZKjUhXcHMrzDzh5n5IAA3AXgjSzqTrjAMoyrEGGX0NIDpRLQLETUB+CSAuZkJiOgAAL9C8DAoSv1ZoyakK4hoAjO/TUR1CCa6XV1MOdLGuzpCoHMU2/VAo3+Cpc9Amy8wIu22aL2Yd6DFpEufwQv1/mDpJLErpRhj5b4oshRaGmmv3W+v1V6aN3/lyk60p8aIMvK/l8hlLgFftlpKUAD+HAPpLwCAwxb+0Nl+YMa3vTTyXAyQ355+0c3y2hlUzqe8LLZL+w6giXWuz+VFRZJ6O3a9D83KTIR0ytVX1yS8dxAvmrvW+fLvywZGutvK9S/lXMak3PZtUKTdW0VzosxV0OZSbBHNmd7v90VvHmmNYohL/ZqZB4noPAS+0noA1zLzQiK6FIFPdi4CE1EbgD+FOp9vMfNxpdZdjgfClQjUSM8GcGdMZZ5CREMhqbcBsOU0DcNIFKyGZhRZFvNdAO4S+y7O+Pvo2CrLoFakK34C4CfFttUwDKPcKEGBNUeiJqaVGzmwTSvund0G3FQ7NfZ4af5U7w6Z37PFHyy+1uyWs8+AH1A4ps617azmEc72GuXsbBTaFY0RAkalWmaQQqx6pZgzJC0RloS6ebEfDj15wG3PB2cud7YfedpXSO0T5oyl9X7/yZXNNJVSeYa1kFJpIjpq4Q+8NI/NcCeL9il24A7R5h3S7vldXu+vYiZ7fTz5vsGRLW45jX2+yejQk7qd7brt2700985xe0NTAR3T6pqnRo3d7KVJr3LzrUCbl+b4U91V8Oqmued41S/9oI9RU926+zf6fXznW245WhjqiduvcbZfX7W9l6a7GOnXPGj9WWsk6oFARFcBOEzs/gkzm4nIMIxEo/nqao1EPRAAPAxgFoC9ABzMzPMAgIg+BOByBBPZ+gF8g5kfrFIbDcMwPOL0IVSLpD0QXkQwce1XYv86AP+PmVcS0d4IvO++ncEwDKNKxBVlVE3yPhAqKVHBzC+Hdcr98zM2FwIYQUTNhU7GaBZnrE2xOcsp9y+ltvPSTBBpOpReHC9M3iuVlc06hM9AXlDaSmxNcnp8vV95FxUe7qghq08roZaynBZt9ThR/WPCZ6BJjPeJNo9jv+4+LeYwT91SviFI4yaS/gIAOHzh5c72jftd7KWRp2Y9uXZqeW0Bvl+rK+Xbtllosg8o19vDt45ytjV/TypCqGVnrzt1qF8u/QdgzYC4bpu8JHjkBrecdnLDkTsw1svTutZtc4+yWqHsHu0mPG+N61tSDqEoifh8DIcHQtRuqZRERRROAPBsXDPzDMMw4oBBkT9JJeoDoVISFTkhohkAfgjgC1m+N+kKwzCqQpqif5JKVB+ClKgYgTJIVOSCiKYgkL7+DDNnla4AMAcArp56mmcckEN2zSQjZ0JqQ0s5C1lTsZTzO7VJ0dKcITtGE0+Uo9/xSuUdDfkXKy8XWpulYqY/69fPI9s8RpEBlTNtoyx8r6mUylnHWkipNBGd+vylXprfHOCm6REzz+UMWo1VDf7PY7W4MrTV7TaKFdN6lHc9aYbTztWbde5PmVLypw1I6512WHImcp+4C3YpqwH2cDx2HHm9aYbFcvwmhkOUUSlnYCkCiQogJomKbBDRaASzni9g5n+Usy7DMIxiSBfwSSqlPBCuBPBFIpoPYFwcjSGijxHRcgDvBXAnEd0bfnUegN0BXJyxtOaErAUZhmFUmDRR5E9SyWu+qbBExe0IzEJy//cBfD9fWw3DMKrFMFCuSNw8hLIize1RDl6zb0cZVnmrSkXwM0QhSt2ajbkYZJsjRHmqaWS/98b0giS1Y6IsWKUN16VKqZSg0MqW/gIAOGv+pXnT5EPzWUn/k3acm7x8+Y9BQ1uBTxJFBbe7Xm67qbTrpE+0L0p7tTRbIly3BSxuFpkkm4KiUpUHgklUGIYx3NDm+9QaVXkgMPO/afuJ6CQAl8CXrpgG4GUAr4ZJn2Tmc8rfUsMwjGiYySh+sklXAMAbzLx/ZZtjGIYRjSTPL4hKTUhXxIWUrtDkpceKePdWxTC4XthHpe0TACak8qcZIcreIMrVZBZ6hcG2o85v4CFb3Ix9igSApI7zv980Ku9AUvJ3hbJ6lmTXftfL8bYSey99N28qOscThHNCzg8B/H73Baj9lc2kbDXgy1DIOQaA7zOQPoU79rnIy7NJxMyPUET1J7HbnmV1/lGMT7l9OoJ8T9IWMYFgaaMvk3FIw0Znu7HRnwCypsuVf3+xyW/P7uIct4uV1+oVw35Tg1vXSkXme5mycp5Eyqe0p/zfSGdD/NoVw8GHUEvSFbsQ0XwieoSIDi+yDMMwjLLABXySSq1IV6wCsFP4UDkfwB+IyFsBxKQrDMOoFnFKVxDRMUT0KhEtIiJPbZGImonoj+H3/wwtOSVTE9IVoZBdX/j3M0T0BoA9AMwT6XJKV8jwQk26Qu7S5Abk+dTWXpLlqOGromzZMZokhjSLjFVX7nK3o72R5L9K86/Npr9hyL5YJxRaNXOap/yqxAmOEJVr4bZRZEZkdIi2spmUPYkiQyFNRMe/8D0vze+FJIam/Lox5faXVvdacfU0KOqwsmjtEJZtdlc/a9zsXz2bhQlSM9WtFmbAdexut6X9H1/PoFtuX5GLmslzvLYM5iGNuMK9iagewFUAPgRgOYCniWguM7+UkewsAJ3MvDsRfRKBxtsnSq27VqQrxoedBCLaFYEJa3E56zQMwygEpuifPBwMYBEzL2bmfgA3I/DbZjIbwPXh37cCOIpicL7WinTFEQAWENFzCA7+nGyOacMwjGpQiJZRpnk7/JydUdRkAMsytpfDXxDsnTTMPIjANO8vMlEgtSJd8WcAf87XVsMwjGpRSJRRpnk7SSRtHkLiqNMM8GJgtlkZqLVGWKlLli2dTVHs8VoazV+RjyhDxWLD6qT0Qr3wPNRF8F9o58HrU80/kKctgO8H0VpTzFBahpRKfwEAnCZktLU0PcrKfpIGETbcqIQRbxShsprNW8pSa9dtFGT4rLcynBK2GxcyvFw7eXHZ+zOJMXpoBYCpGdtTwn1amuVE1ABgFICOUiuujLdFQERXZaiWDn0+W422GIZhxEGMUUZPA5hORLsQUROATwKYK9LMBXB6+PeJAB5kjjCZKA+1Il3RCOAaAAciaPMNzPxflWmtYRhGfmITlWQeJKLzANyLYE2sa5l5IRFdimAC8FwAvwHwOyJaBGA9godGySTNZJRNuuIkAM3MvA8RtQJ4iYhuCv0bhmEYVSfOCWfMfBeAu8S+izP+3oLgvhgrtSJdwQBGhrayEQD6AXTlbHeEJSs1W/tIYX9sVaQE1oolKvfq863r0l6r1dUkit4svtds9rIcLYZ/rMioDVFl2VEu5vyiAb70MACME304KeVKMazlJi+PtL93KhIdU0VnNGoS4+JaUlbixHYiJn489XlpulJuULy21KWUrpZ2dG2OgfQZSJ8CANw/49vOdk+dX7eU2xhRrxypWA5Tk2/Ymbc4241Kv3cOuvM0Ouv99kwj92pubnTfnzf1+ed8QMRjdpNf7opG9yrUfEJTUu75W6NcX5uVc1Eqw0HLqFakK24FsAnBjOW3AFypPVScmcqbbKayYRiVY1taQrPa0hUHI3i52xHALgC+Fk5Qc2DmOcw8k5lnHj5yehHVGIZhFMdw0DKqCekKAJ8CcA8zDwB4m4j+AWAmcsxW1mYDSqmDdUosY5swMWjhe3Wi8EVN/nN1x0E3X0oJZmwWZUvlUK2jpONqOfnrrr037ZoL+hUZA609Evkm06IYXAbEO4WmoClpjLD0mhQ37VHeq1pYmOWUY5ImNmmmA4CJda6ZZGSLr3bKYpm31crZkeYLqVIqJSgAP6RUmocA4EMLf+Bs36SEpo5qdutqb9/ipela656bDuV9cOyoXmd73PReL82yBaOc7cVpT1bMQyqZBj/l3PT3+detvHS0t22pTCt/ZwCwOcL1XyiDib7VR6MmpCsQmIk+CABENBLAoQBeKXOdhmEYkRkOI4Raka64CkAbES1EEKP7W2ZeEEedhmEYcTAcfAi1Il3RgzKEWBmGYcTFcIgySto8hLIi7e/jlDMopXO10LzWCHbMjfVuQZrZXKaJcjKk1PD0lG+zf63B3RfljSTaUDF/CzV56S4R4rdEuJw2K5XLNk9L+3WvE7u0iUHSl6SFKb4Id2WuRmWlroEi6pIrm0WRzNZCSqXP4BQlNPWWfd00DR1tXppeES6tnat5XUIf7RlfL036ZbRV6F4XfTq4xd3W7Po9MhQ0gvy15iuUshhaaHYExdGCSSfaGBSNqjwQiOgqAIeJ3T9h5t9Woz2GYRilUvuPg+RJV1wB4P8hmHj2BoDPMvMGIhqLYC7CewBcx8znVayxhmEYERgOUUZJMxndD+BboZbHDwF8C8A3AWwBcBECX8beOfLnZJ2wF9QroWcThS3g+COkyCDw/f+b6Gy/a9APj+sUuz6sTKzecXd3QfPbX53qbPcoJgY5XNcuwcli5rScrashVw3TaFKG+dK0I2dxA8DOg27I4VGXTHC2/3Lpurx1P9/ghyl+ZaTbp4vWj/bSbKqTM1v9Y9iO3fYdelK3l+bhW91Qy411/jnfJM6XXPhermoG+CqlcsYx4IeUSvMQAJy8wDUjpZbM99L87bjbnO1+5ZzvDjfMdFSbH74qu/DFnjFemo9eMc3ZTi9yI8Q3/3Oll2fjMteU2NUlo9mBp9LbOduaGuuhI905qy90++0rx0zl2n8cRDAdE9E0InqZiH5NRAuJ6D4iGkFEDxPRzDDNOCJaGv59BhHdQUT3E9FSIjqPiM4novlE9CQRbZ+tLma+L1zsAQCeRCD7CmbexMyPI3gwGIZhJI7hEGWUZOmKMxHoJUXGka7oMekKwzAqRxoc+ZNUEildQUQXIgjkuDFi+wAI6Yo2k64wDKNyDIeJaYmTrgjVUj8K4Kg4FnzIRb9yahpFPFrfKj/NgLfil4+0bfYO+DF0mzvdfbIc7eBlm/uVeFYtpC8ONBkPOfz1QgehSEqwm0tbDU36NFazby3ctEkLeMzdPih282aRqm57X4qhRbS5Rz3rbtkjyPUhNCgSIrJPNZVSKUOhhZRKn0H9Lgd4aUakb3W26+r99mw3wlUKHTVBavACfT3uT7ipWzmB7a7dnsa4PpjGCW97Wdo2u3Vr0hVSEli7dtrGuP3VutE30mxWfEClkmRTUFRKcSovRSBd8RRikq4gomMA/AeADzCzL6JiGIaRUFKJfvePRikPhCsB3EJEZwO4M6b2/BzBPJf7wzURnmTmcwAgdFq3A2giouMBfJiZX4qpXsMwjJJIsm8gKkmTrtg9x3fTsn1nGIZRbWr/cQBQmc30VePqqad5B9YijHwbFTOiTCOlIgCgW5iPtYjmdlHO5igrpkVw8UsLszZXYfd+t+BBdcU0d2ddhMtZs9dK9Q8pxwH4sh3bp9wdcnU0wJ9v8baiOTFB014QyCRa3HqbaI/mK0kJ34PWZilNsVu/O3digyJLIX0uo1K+JVrOk1je4PujthOrs41I++V8eOFlzva1+/vzGd49KKTAG/35Hxv6Xd/Na03+imTjhPx7m5Bkb1Is7nKFtF6lj7XrS9IqitauW/lb+/zy35c8MeEL006KfDP91dI/FV1fGLr/RwQBOksBnMzMnSLN/gB+icCqkgJwGTP/MV/ZpaidFg0RXUVEz4nPZ6vRFsMwjDio4DyECwA8wMzTATwQbkt6AXwmnCpwDID/JaLR+QquCemKjO93AvASgEuE6cowDKOqVNCpPBvArPDv6wE8jEDR4R2Y+bWMv1cS0dsAxiOYR5aVWpGuGOK/UeBktUzkkF4zH0gVRH+5dV+EUVMylSYiTV1RihREGa7JNKOU140NYlitvZH4dRU3gpVlR1gMDesjDPtlf41VlGmjvGnJ9khTFAB0ef1VePs05Opx2vmVqqmauq5c2UyzlEkZCi2kVJqIznzOV029TqSpY18+YjCCCmmH6NOO+sJvNcUqkvZKk26F7tNcwAMhDMY5O2PXHGaeEzH7Dsy8Kvx7NYAd8tR1MIAmBC/ZOcl7lohoGoKb8OMA3gdgBYIn1N0Avs7M84hoHIB5zDwtnEdwPICRCGY4Xxk25tMI7q8fYeb1UGDm+zI2n0RGOGsYWbQEwKZ8bTYMw6g0hZiCwpt/1gcAEf0dwETlqwtFOUyU/ZFHRJMA/A7A6cyct4k1IV1BRG0IRgr/mSuDSVcYhlEt0syRP/lg5qOZeW/l8xcAa8Ib/dAN35/lF3zXjmBKwIXM/GSUY6gV6YpLAPxPuHJaVky6wjCMalFB6Yq5AE4P/z4dwF9kAiJqQrD65A3MfKv8Phu1Il1xCIATiehHAEYDSBPRFmb+ecT2AwCaxYBpWYN/aiYJA+3YQX+UtbjJfY5qAgrStu8H7wHtItxxtZCOlmGpgB/yuqLOX7tr9mY3xG8LfHuyPCrtzUCmaVQuZRb29hUN/ultEyGRE9n1zHSzn2ejsIG/qHTG/tpSWAIZHipDTAFgB9GeMa2+TEZnr3uJv1nn29ZlKOMhDa68+bLNvuSEXN1rZ0WiY+wod9K+t6oZfNlqKUEBAB2bRjjb0l8AAGcIv0LPF8/00iyf50pQPzI4yktzYMrVmJgwxn2X6+r2+6+lyb2WV/X4/fV6k+vA0IwlOwq5dU3yfH0RPo18VHBi2uUIJgWfBeBNACcDQKg+fQ4zfy7cdwSAseE9FQDOyHixV6kJ6QpmPjwjzSUAegp9GBiGYZSTSkUZMXMHgKOU/fMAfC78+/cAfl9o2aXMQ7gSwBeJaD6AcSWUk8nPAWyHQLriOSK6OqZyDcMwyspwkL+uGemKjDSX5EtjGIZRaQoJO00qSZuHUFY8qQhVOprEtp+mT+RLK5LKkwdcC/zGen8w5kskuOX0KyZy2eb1indibKs7w6G3zw8cT0UI8maRZoQiY5BKu8f1miLPII9rdJNr3+7q9dsn5wIsUcRv90ers60tCSnPnybPvWude1yjxvqSz/0Drk+DUr4NXNLY6NqyGzdrcutiu873WY2bLo79Gd+HIJe61GSrt7zpnhttjoH0GbT98lovzYQT3TT8hu9DkD6D7ae77Wta4fu+mtuE7+sN/1oizj+3Yyy517/0cwFAZxlufdu6/HXRENFVAA4Tu38C4N1QZioT0akAvpGRdl8AB+ZzkBiGYVSK4aALlzTpig9DmanMzDciDEElon0A3GEPA8MwkkSSfQNRqZmZyhmcAuDmCMfmIZVBx6Xz+9QVxQRvaLiR/MHigDBfRFmfSZq0ZIgp4A+R9+QRXppX+lxTgGZK8RYxU9oju6d50A+w9dRONWlJUVlDg2sa2KDILEhzWSP5nbFZmH/8tcYUSQclzbKBkc52epXfX2sG3H5WFj/zros1XW65m5VjkHQqfbxsgWuS0eQ35MupXNUM8FVKNQkKGVIqzUMAsP2trhlp1H5++GpTq2sS6l4mTD2Keu2G1W4fd/YrZrkIshmtTa4J8NX+7bw0mjm2VIbDAjk1MVNZ8AkAN0UswzAMoyIMhyijWpmpPLT/EAQPlxez5DPpCsMwqgIzR/4klagPBDlTuQHln6l8Kvs990nkGB2YdIVhGNWigushlI2amKkcfleHYDr24VreKEhZXO3gpRpCh2LfbhWPqfGKo2GtKFxbea1ThKLKp3OLkoeEPX6EkkZKYGjm0qLeUZSQTYnWZsmr3aOd7W7FLix/NDMUX4k8n34go9+nWvOWNbqpVsCXTEiLRcG0Nym578Um12avXQOSTkVSYXG63S1Hyfdizxhnu6nbr2xVU/73PylDoYWUSp/BKc/7Mtr37O2IcmJlg/s7Gq9IwiwR7VMWYvNcCGmlT5f2uedvZZMWjuznK5XhMA+hlmYqHwFgGTMvjqkuwzCM2EhxOvInqdTMTGVmfhjAofnaaxiGUQ2S7CyOyjY1U1mag7QV0yaIoex+E9d6af5zgzuEP6bPD49b3uhWtne/rz65Q7u71s8Tm7d3ttcpIZyDcna1Ejq7g7CdaGGKEqnUqTFCebGRRT/VKNeBA/YbdMf+H77IPc6b/muDl0ce+m3wz8O5A+6M3T5t4XsRdjqo9EWbOK7jT/XXYHrkBvcca6Gy3WLX7v3uiVitKMGOEDaPaeTPMJa8LmZoA8BHr5jm7mgf46W58wvznW25qhmQX6UU8ENKpXkIAI558TJne+C2n7nb/1jg5Rlc75a7YYn/u/r7hvFuW5Tf8JHHussDpO+e4KWRKrNxMBxMRomaqczMv61GewzDMEolysI3SSdpM5W/h2DSWxrBKkBnhAtEEwJpi48A6A33P1up9hqGYeSj9h8HpTmVy8EVzLwvM+8P4G8AhsIZjkUwOW46goWpf1md5hmGYegMh4lpSZOu6MrYHImtD93ZCJaCYwBPEtFoIprEzKsKOdjRwla8Upk+3yvs0D1dfpDfaBH8pslbyLC2XkW8YtNm17Yubf0tSsDoRnExaSumzRAaDn2KZII88lbtIAQtEYbEUxv8WEEZGpt6YZGzPSrlq3fKvtitvt1Loy5DJ5C9rq2wNSbldljdtMlemnZa7Wz3Kf3VLezS7UJFdZ2yMpw8M82N/vlsElIfg1t8H0J6kRt8R2P8cNG2tFtOhxLimk+lFPBlKGRIKeD7DBo//iW3jD/4khgjdnOvnbZu3++GDf4uyeA6t9/HKRdKHysxrSWS5OihqCROuoKILiOiZQBOxdYRwmQAyzKSLQ/3GYZhJILhMEJInHQFM1/IzFMRyFacF7F9AEy6wjCM6sEF/CsFItqeiO4notfD//2Qsq1p24loORFFWnI4cdIVGdyIrSORFQCmZnw3JdznYNIVhmFUiwpqGV0A4AFmng7ggXA7G98D8GjUgpMmXTGdmYde7WcDeCX8ey6A84joZgCHANhYqP8A8OchjFLswCPSrh2ws1eR4BW7pN8B8GPbu+t8O2vzgOufaBTz8vuVNwlZynjFLt0jgvj7FPlrWbIWn++hmEhlF64m3wZOIv5+YJUb675RiYeXpSwYWOel2a/RtRoqLiHPF6G9Ack5Bat+6Y8uO+D6ObQ4dumfqBc72tJ+B8pyNvX5tm1m1waureK3+Z8rne3GCW97aZrg+2q89nS7F7e2spmUrtZkKOQ8A+kzkBLaALDsyHOc7bUd23tp5AnUrtuut9w+XFHn96n2my2VCpqCZgOYFf59PYCHAXxTJiKigwDsAOAeADOjFFzKA+FKALcQ0dkA7iyhnEwuJ6J3Ibj1vAlg6Aq5C0HI6SIEYaefjak+wzCMWCjEqRzeN8/O2DWHmedEzL5DxgvxagQ3fVl+HYAfAzgNwNFR25U06QrVWR1GF6lzFwzDMJJAIb6B8Oaf9QFARH8HMFH5ypkWzsxMpMXO4VwAdzHzctIWyMrCNiVdIelW+nFQdN6mtN9FrWLcqklg5CsXAHpFOKg0bzQqYad94qLrUlZra5WmCcVcJYkQwamHnYpdsm+UJOh60x3Ca+9VspSdGvwwyg5R8ljFBJgSu5RARrRKU+JUP9Syda3byh72j1OaJGW4aM9gfpfdAOf/8fYo5o6Ny1xTT9tm/0i7Kf/PvaXJNRHJhe8Bf2UzqVIK+DIUMqRUmocAYOpDVzvbjcd+3kvz8ko35FaTZZEmrZ3ZP5+LFPXcUolzpjIzZ32rJ6I1Q2H3RDQJwSReyXsBHE5E5wJoA9BERD3MnMvfYNIVhmEYcVBBLaO5AE4HcHn4/1+8tjCfOvR3ODdsZr6HAVA70hWzEXjL0wj8jV9h5scr1V7DMIx8VFDL6HIE/tuzEPhaTwYAIpoJ4Bxm/lyxBSfNZHQFM18EAET0ZQQT085BEFo1N7SX7QvgFgB7Vq+ZhmEYLpUaITBzB4CjlP3zAHgPg3y+20xqQrqCmXu0/YUi5STaNBu92NWgyD60CDtvo/JmsE6EUo7xTbGepIQXtuhn8erWrNKbhM+gP4JTKYpEdjpCqF6b0hdSorhzvS+9kI+p5Nt8RwifgeaL8EJRlUOQx96/0e/VHuU6kEjz/8o+9zj7lJXhJJqdv79PXAlKOV1drg/BywM/1FJzV6zqcVcb2/KG357OfhGaqqhASOlqKUOhhZRKn8HEu3/tpRnY/2Jvn2T1alfm5G325WeUn2PJmHRFduKWrgARfYyIXkEQ4uoLoRiGYVSRNHPkT1KpGekKZr6dmfdEMPr4npbXpCsMw6gWlZKuKCe1Il3xDsz8KIBdQzOV/M6kKwzDqArM6cifpFIT0hVEtDuAN0Kn8oEAmgF05CxLC5kXNtM2JU2PeERKSWMAGC/O50bF2C/NvD1Kmh7NoJ1Bu3rd5Lebd4izqk5bEUQIf4cfze0zRpkLIHvwKd6u4Lp3TeWfSxHl7aZB6Qs5V+HOt3wh3VQE+79kWVP+NktWNPp5opy/p9Jun0JZiZMjNOf1JvdAiZUDb8y5CcBf6tKTrVZOljfHQPEXnPHcpc72bw7w08yvFz6qwk9DUSRZxTQqtSJdcQKAzxDRAIJL/RMcg0KUYRhGXAyHW1KtSFf8EMAP87XVMAyjWgyHKKOkzUOIDc0MIU9XFBODNlyXJgYZzgr4oYxxmW2KueSilFtJZBiqr6epHKdyDDKktNjjlOdGMytF6fc4+llTbJV1a/VI+ZQ6pRwZUq0h+yLKbySt1CXPsURTKY0S+ixNRGfNv9RLc4MwNWnXVzl+E0mOHoqKSVcYhmHEQJKjh6JSE9IV4XezAPwvAh/WOmb+QCXaahiGEYXh4EOIGnZaKa5g5n2ZeX8Af0M4MY2IRgP4BYDjwslxJ1WthYZhGArDYU3lmpCuAPApALcx81thOk3uNS+evLRyXiaK1Z9aFEfRvUJF4dB+P65tdYNb2QRlrrxcQWtdg/t8XqkYlGUxU6VDA0Cv2KVEgno2Zs2+LHdpF4ss5+X6fi/NbmlX2+Djey5zth94eYqXZ4uQ23iwvtdLcyC74YWtiqFf2oo1e/IW8Vp04vZrvDTz1kxwtjUJ6i1iV4toj3Kq0CzSTEn5stXryQ3s1FZrO3Sk+5NqG+MHCT+4fEdnu1d5Hdxx0L3CxpJ/PlubXLH0pX1tXpojj3V/ooPr3DxyVTPAl62WEhSAH1Iq/QUA8BkRmnrTfn6aKHLvhZJSVsSrNWpFumIPAGOI6GEieoaIcpZhGIZRaSq4pnLZqBXpigYEk+D+FcC/ALiIiPaQeU26wjCMarFNmIxCpHTFCFRGuuIuAN8FsBxABzNvArCJiB4FsB+A1zIzZC5Ld/XU07xef7bOnb65ItUjk+C0eneG5WZlZax3CbtNl/JYHS3SyBnQAMDieSxNPdqs3zfqXaPH999+zEvzyu57O9u9vf7wXAqgboogxdnSqCy4LmxG6bS/spk0nTz6kmsi0mqW/bWLonbaKWIk03lmfgP6G9D0freBr6/ylTgHhFVQ+0nLkM32lFvu2galdrFrDfvnqlm8UWrmvRe6xzjbrRt984VcOE8Lha4XdbHSp6/2u7OiVzb5adJ3uya2ccJIoy18L1c201RK5axjzQQoTUSnPO+Hpj4w49tKztJI8pt/VEpxKi9F8NYOxChdkbH5jnQFghWB3k9EDUTUCuAQAC/HUadhGEYcDAe105qQrmDml4noHgALwu+uYeYXY6rTMAyjZLaJeQhJkK4Iv7sCwBX52msYhlENhkOUEQ0Hu5eG5kPYJEyd2pG3ip2aLIW04UaRrtBCXPPl0WzF0ma6STEE7yoK0sJOJVEu5UgyBso+KQUhZQvGpPxcG0VopeaDaRPZokgfaLQK7YWUtpKeqF87ThlWKs+5Zu+WaNeSRIbJAnrIrUReT1qWkWKn1qX9YufmCO2RfhC5ehvg95e2qpk8DxqynMkDfs8ftfAHbp5xu5YsZtHcMjXyzbRvy7Ki6yOi7QH8EUGAzlIAJzNzp5JuJwDXAJiK4Hb3kfAFPytVmZhGRFcR0XPi89lqtMUwDCMOKhh2egGAB5h5OoL15i/Iku4GBJN99wJwMAL1h5wkSrrCMAyjVqmgtWU2gFnh39cDeBjANzMTENG7ATQw8/1h2/yQSoWkSVcYhmHUJFzAJ3POVPg5u4CqdmDmVeHfqwHsoKTZA8AGIrqNiOYT0RVElH+poEKGObX6AXB2UvMM17qsfbVTV9LbV+m6kvAB8HcALyqf2QA2iLSdSv4TEUwK3hWBJejPAM7KW2+1D7xCnTsvqXmGa13WvtqpK+ntq3RdSf8AeBXApPDvSQBeVdIcCuCRjO1PI5Afylm2mYwMwzBqi7kATg//Ph3BxF3J0wBGE9GQ9MIHAbyUr2B7IBiGYdQWlwP4EBG9DuDocBtENJOIrgEAZk4B+DqAB4joBQQRxL/OV/CwXUJTMCfBeYZrXda+2qkr6e2rdF2Jhpk7AByl7J8H4HMZ2/fDnTycl2E7Mc0wDMMoDDMZGYZhGADsgWAYhmGE2APBMAzDAGAPhEQQrkltFAkRja12GwxjODDsHghE9PFcnwS071giWkJEjxPRAUS0EMA/iWg5EXmRAxHK+1ABaX8QIc1xRCRXwItS9igiupyIXiGi9UTUQUQvh/tGZ8kzkYh+GYodjiWiS4joBSK6hYgmZclz+dADNAyzW4yg/94kog/kaF8bEV1KRAuJaCMRrSWiJ4nojAjH5i3qVshDPKz7wGz9kJFup6E0RDSNiE4kor1z5clR1p4Fpj83z/cFRaso+XcI++BAItKkFvLl95exy51+dyI6IdT0MaJS7Vl3ZZjF99vwcyeATgRTtv8MYD2Av2XJMxXAzQAeA/BtAI0Z392RJc+ZGX9PQaA6uAHAEwD2yNG+5wDsBeC9ADoAHBru3wvAs0Uc71tZ9v9UfH4Wtu+nAH6ao7zNANYB+B2AjwCoj9iOexEIbE3M2Dcx3Hdfljz3APgSArXGBWHaqeG+v2TJ80LG3w8BeE/49x7IMTMVweSdM8JzdT6AiwBMRyAO9oMseY5EsHzrOgD3AZiW8V3WcwXgFxl/vx/AW2FblyGQINbyXABgCYJVAj8X/v8bAAsBnB/XdRF+d774fC08xvOz1YVAjfp1AN8D8O4C2rE/gCcRrHD49/DzSrjvwCx5DgvTL0SwOuL9AN4I+++9WfI8BGBc+PenESyvew2Cddy/VGj/baufqjegbAcW/IAnZWxPAnBvlrT3I1idbf/wxvkEgLHhd/Oz5Hk24+9bAJyNYMT1MQTStNnalZlvmfjuuSx55mb5/BXApix5lgH4PYDPIJjNeDqAtUN/52jffABjAHwewUNuDYCrAXwgT3970+fzfZfZt/IGlqMvXkag4ggAT4rvXsjRhufF9tPh/3UAXsmS52kAM8K/TwxviIfKtuc5xw8N3fgQ6MqoD63w5jcCwFgA3QDGh/tHAngxSx750M98+HflaF83Aj39ixGsWf5dBC9P3wXw3RzXxd4ALgOwCMDzCB5i0/JcF88BOETZf6g8JxnfPQVgHwQvTesAvD/cfyCAf2TJ82LG309j6++3FcCCXG20z9bPcJ6YNpW3KgICwY1tpyxpxzPz1eHfXyKi0wA8SkTHQV9HR7IHM58c/n07EV2cI+0GIvoCgHYAnUT0VQQPlKMBZJOoPRzAacr3hEDnXOPdCN7mjgHwdWZeSUTfZebr8xwLc7DYxq8B/JqIJgI4GcHyplOYeWqWfG8S0X8AuJ6Z1wCBmQDBW/myLHkyTZY35Pguk18AuIuILgdwDxH9BMBtCKbmP5fjuDYR0fuZ+fHwvK4HAGZOE1G2xUqamHlhmO5WInoZwG1E9E1Euy4AoJ2Znw3LWExE2Y4rxcybiagfwSitI8yzKXvz8FkEb/d9ynen5GjTDAA/RvCw+U9m7iWi05n5P3PkYQ6Wrb0QwIVEdDCATwJ4nIjeYub3Zck3kpn/qRT2JBGNzJKnkZlfAAAiWsvMj4d5niWiEVnyDBDRZGZegeB3sinc3wcgv8qnAWB4z1R+gIjuBXBTuP0JBMNVjUYiamHmLQDAzL8notUIzCDZLtopRPRTBDfl8UTUyMwDQ+XlaNfpCJYbTQP4MIIf7r0I1pD+fJY8TwLoZeZH5BdE9KqWgZm7AXyFiA4CcCMR3YloPiPn7sPMqxG+eRLRzjnyfQLBG+MjRDQh3LcGwUjm5Cx5/kJEbczcw8zfyTim3REM+bXj+lk4Ff+LCMxEDQhMP3cA+H6O9p0D4Boimo7gbfzMsK7xAK7KkmeAiCaGfQBmXhj6ef4GYLccde1JRAsQ9OU0IhrDzJ3hw6ApS55niegPCK63BwBcT8E64rk0aJ5G8Gb8hPyCiC7J1jhmfgvASUQ0G8D9RPQ/OY7lnSJFGU8BeIqIvgbgiBz57g6vvRuw9cVgKoKR6z1Z8mRep98S32Xrv68CuI+I/ozg/D4Y/v7fj8CEbERgWM9UDp3Ih4ebjzLz7VnSfRXBMP8Rsf8AAD9iZs9xS0Sni11zwx/9RABfZuZvl9j2bzHzf5VSRkZZBOBcBPbX0/KkncXMD8dRb5byT48wSqlKHpmPiI4GsJaZnxdpRgE4j5kvy1KGfHCuZOaB0BF9BDPfpuRpAHASgpHHrQhGfp9C4H+4ipk3KXm2B7CFmXsLPc6MMkYCuASBWSfrjZ2IPsXMfyiyjo8AOA7A5HDXCgS/l7uypD8OwN/lcRHRbgBOYOYfZck3CkGfDb0oLEfgi3qlmHZviwzrB0LcFHOTLvbGTkTPMvOBheZLOsUcV6XylJLPMIYDwzHs9PHw/24i6sr4dBNRV4nFn1ShPIC+vrmfKDCfFFZwEXlKySeLSXCeyPmI6O6iCi8iX7Y8RNRORP9FRL8jok+J736Ro7yC85VQlxZavIByhxYXE45ccD2Gz7DzITDz+8P/tytD8ZW8Mb0zdKPs8ycIQWin/0UReUrJVwDFDEkrlcfJR0TZRgqEICJN/7KIfEXW9VsEkU9/BnAmEZ0A4FPM3IcgiicbxeQrtq7rEISAj0QQcXUjgH8FcDyC6LXZEfN8pIg8+eoxBGYyKoAKmy7mM/MB4d8DCC5w7WSdqD38islTSr6oZB5X0vLIfESUAvAI9If6ocysRrwUk6/IPM8x8/4Z2xciuHEeB+D+bNddMflKqCuzP99i5p0yvnPKrHQew2fYjRDKTGwjBCI6jJn/kWPfnzK+WgDgyjDsT5ZzdJZ6i8lTSr6o/CN/kqrlkfleBvAFZn5dJiKibKG0xeYrJk8zEdUxcxoAmPkyIloB4FEAbTnaV0y+YusqJrS4UnkMgT0QMijwJl10npCfIZhoo+5j5kyZia8AyOb/+FiW/cXkKSUfAIAC6YXPAJiGjOuLmb8c/n9etfIUke8SZL+ZfCnL/mLzFZPnrwjCUt8Jp2bm6ygImf5ZjvYVk6/Yuv5CBYYWVzCPITCTUQaaeSefyafQPET0XgDvQ3DjzYz/bgfwMWber4T2VzIKSs1HRE8gmDfxAoK5FgCAXCGglcpTSr48ZZYc4pq0PMO1rmLbt61gDwQUd5Mu9sZOgQDbLAQTpa7O+KobwF81k0EBx1H18MxKtSHudpdCJduS9L5Kel3lOP/DCTMZBTQhsIM2AMh0mnYh0LCJKw84mPz2CBFdx8xvltJohSSEZ/6OiD6PYDbvO5IKzLw+R1mVylNKvlyUNcS1SnmGa13Ftm+bwB4IKO4mHcONvZeIrkCgK/OO3DQzf7CIst7JXqE8ufL1A7gCgeYNZ6TdNUdZlcpTSr5cxN2HScgzXOsyk0gO7IHgUsxNutgb+40IFCc/isB8NKRGWgpJeKP7GoDdmXldAWVVKk8p+XIxXN+Kh2NdNkLIgYVjudyIQKt9FwD/CWApAgGxuPMAgTzvbwAMMPMjzHwmgiiOrBDRYXn2qVFQheYpJR8CaeRCtXUqlaeUfLmII8Q1aXmGa13Ftm+bwJzKGRDRM8x8EBEtYOZ9w31PM/N74swTpnmSmQ+lQJHxpwBWAriVmbOqaFYiCiqGfLcjGC09BNdG/+Vq5ymhrtHIEaoaZ75K5RmudRXbPiPATEYuQ/LVq4joXxHcpLcvQx4A+D4F6oxfQxDH3Y4gYskjI6JpPBGdn/FVO7JovReTp5R8GdwRfgqhUnmKzXcXlFDVMuWrVJ7hWlex7TNgDwRJ5Jt0iXmAQPTucQ5mBB9JgZTxlQgmAEkqFgVVQj4AxcXzVypPCflamPn8/MliyVepPMO1rmLbZ8BMRg5EdD2Af2fmDeH29ghkHM6MM0+Ybj4LrR1tn/h+50IjmorJU2K+JVAiOZg5axRPpfKUUNdXEazCVVCoajH5KpVnuNZVbPuMABshuOw7dGMHgouIgkVy4s4DAHUUrqQFvPMgyXc+KhkFVWy+mRl/tyAYCeUzoVUqT7H5KhnimvQQ3KTXVY6w4m0HTsDCzkn5IFg4fEzG9vbIsXB7sXnCdJ9BEJ30vfDzCoBP58lzH4CzEAihfQDAtQB+GHeeUvJlKeuZpOaJkg/AYgDjiii34HyVyjNc6yq2ffYJPjZCcPkxgP8joqGQypMAqMsklpgHzHwDEc3D1lDTjzNztrVzhxjLzL8hon/nrRPj8oW4FpOn6Hzk6vrXIXgjz3mdVSpPCfkqGeKa9BDcpNdVjrDibQZ7IGRQzE26yBv7UN6XkH0BdY1KRkEVm+/H2DpUH0QwLyPfqnGVylNsvk0AniOigkJci8xXqTzDta5i22fAnMo1BRF9FMBjAKZia0TTJcysRSYVnafEfC0AToAbB87MfGm185RQ1+nafs6vrFpwvkrlGa51Fds+I8AeCDVEhaOgis13D4ANAJ4FkBraz8w/rnaeUvIZxraAmYxqi0pGQRWbbwozHxMhXTXyFJWvwiGuSQ/BTXRdxbbPCLAHQm1RTKhqMXlKyfcEEe3DzC9ESFvpPMXmq2SIa9JDcJNeV7HtM2Amo5qCiD4D4NvYKix3EoDLmPl3ceYpMd9LAHYHsASBU48Q2Oj3rXaeUvIp5TzDzAcVkqfYfJXKM1zrKrZ92yI2QqghKhkFVUL01LER0lQrT1H5KhnimvQQ3KTXVWz7jAAbIRhGHsIQRhmqeiUz51y8vZh8lcozXOsqtn1GgD0QDCMPFQ5xTXoIbqLrKrZ9RoANpQwjP3dga6jqljLnq1Se4VpXMXmMEBshGEYeiOhFZt67EvkqlWe41lVs+4yAumo3wDBqgCeIaJ8K5atUnuFaV7HtM2AjBMPISyVDXJMegpv0uuIKK95WsQeCYeSBiHbW9nOeBYSKyVepPMO1rmLbZwTYA8EwDMMAYD4EwzAMI8QeCIZhGAYAeyAYhmEYIfZAMAzDMAAA/x9LceD2DuWp+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train_data.corr().round(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "55e449fc-b256-4297-ab07-017993b74732",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data.corr().round(3).target.iloc[:-1].plot(kind='barh', figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "499a3a48-1b84-4e7c-9d9a-d26622c02a04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    789400\n",
       "1.0     10600\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0042938-e84f-4648-a06b-56b3346b13a3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id        0.0\n",
       "cat_0     0.0\n",
       "cat_1     0.0\n",
       "cat_2     0.0\n",
       "cat_3     0.0\n",
       "cat_4     0.0\n",
       "cat_5     0.0\n",
       "cat_6     0.0\n",
       "cat_7     0.0\n",
       "cat_8     0.0\n",
       "cat_9     0.0\n",
       "cat_10    0.0\n",
       "cat_11    0.0\n",
       "cat_12    0.0\n",
       "cat_13    0.0\n",
       "cat_14    0.0\n",
       "cat_15    0.0\n",
       "cat_16    0.0\n",
       "cat_17    0.0\n",
       "cat_18    0.0\n",
       "cat_19    0.0\n",
       "num_0     0.0\n",
       "num_1     0.0\n",
       "num_2     0.0\n",
       "num_3     0.0\n",
       "num_4     0.0\n",
       "num_5     0.0\n",
       "num_6     0.0\n",
       "num_7     0.0\n",
       "num_8     0.0\n",
       "num_9     0.0\n",
       "num_10    0.0\n",
       "num_11    0.0\n",
       "num_12    0.0\n",
       "num_13    0.0\n",
       "num_14    0.0\n",
       "num_15    0.0\n",
       "num_16    0.0\n",
       "num_17    0.0\n",
       "num_18    0.0\n",
       "num_19    0.0\n",
       "num_20    0.0\n",
       "num_21    0.0\n",
       "num_22    0.0\n",
       "num_23    0.0\n",
       "num_24    0.0\n",
       "num_25    0.0\n",
       "num_26    0.0\n",
       "num_27    0.0\n",
       "num_28    0.0\n",
       "num_29    0.0\n",
       "num_30    0.0\n",
       "num_31    0.0\n",
       "num_32    0.0\n",
       "num_33    0.0\n",
       "num_34    0.0\n",
       "num_35    0.0\n",
       "num_36    0.0\n",
       "num_37    0.0\n",
       "target    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5fbe3533-8b30-4c24-824d-c43596bc5e0b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 10600, number of negative: 789400\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.463075 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 9433\n",
      "[LightGBM] [Info] Number of data points in the train set: 800000, number of used features: 58\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.013250 -> initscore=-4.310419\n",
      "[LightGBM] [Info] Start training from score -4.310419\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "m = lgb.LGBMClassifier()\n",
    "m.fit(\n",
    "    train_data.drop(['id', 'target'], axis=1),\n",
    "    train_data['target']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "de364bba-9285-49c6-8454-237602d08c50",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd.DataFrame(\n",
    "    {\n",
    "        \"id\": test_data['id'],\n",
    "        \"target\": m.predict_proba(test_data.drop(['id'], axis=1))[:, 1].round(4)\n",
    "    }\n",
    ").to_csv('submit.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d14e614e-6607-4178-bbd4-cd5a781e799b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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